Bubble Chart

Hanna Rodrigues Ferreira

18 outubro 2021

data <- read.csv("owid-covid-data.csv")

data <- data %>% mutate(cases = total_cases, 
                        deaths = total_deaths,
                        vac1 = people_vaccinated,
                        vac2 = people_fully_vaccinated,
                        pop = population)

data <- data %>% select(continent,
                        location,
                        cases,
                        deaths,
                        vac1,
                        vac2,
                        date,
                        pop)

data <- data %>% filter(!(location %in% c("World",
                                         "Asia",
                                         "Europe",
                                         "North America",
                                         "European Union",
                                         "South America",
                                         "Africa",
                                         "Oceania",
                                         "International",
                                         "Northern Cyprus"))) #NA's

glimpse(data)
## Rows: 112,772
## Columns: 8
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, ~
## $ location  <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanis~
## $ cases     <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 11, 11, 11, ~
## $ deaths    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ vac1      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ vac2      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ~
## $ date      <fct> 2020-02-24, 2020-02-25, 2020-02-26, 2020-02-27, 2020-02-28, ~
## $ pop       <dbl> 39835428, 39835428, 39835428, 39835428, 39835428, 39835428, ~
summary(data)
##          continent            location          cases              deaths      
##               :    0   Argentina  :   630   Min.   :       1   Min.   :     1  
##  Africa       :30055   Mexico     :   630   1st Qu.:    1796   1st Qu.:    63  
##  Asia         :27371   Peru       :   630   Median :   16850   Median :   482  
##  Europe       :27795   Thailand   :   627   Mean   :  457126   Mean   : 11681  
##  North America:15185   Taiwan     :   615   3rd Qu.:  165550   3rd Qu.:  4085  
##  Oceania      : 5398   South Korea:   610   Max.   :42410607   Max.   :678407  
##  South America: 6968   (Other)    :109030   NA's   :5685       NA's   :16223   
##       vac1                vac2                   date       
##  Min.   :0.000e+00   Min.   :1.000e+00   2021-06-21:   219  
##  1st Qu.:1.559e+05   1st Qu.:7.920e+04   2021-06-22:   219  
##  Median :9.441e+05   Median :6.225e+05   2021-06-23:   219  
##  Mean   :8.855e+06   Mean   :5.664e+06   2021-06-24:   219  
##  3rd Qu.:4.753e+06   3rd Qu.:3.372e+06   2021-06-25:   219  
##  Max.   :1.101e+09   Max.   :1.022e+09   2021-06-26:   219  
##  NA's   :89772       NA's   :92634       (Other)   :111458  
##       pop           
##  Min.   :4.700e+01  
##  1st Qu.:1.933e+06  
##  Median :8.715e+06  
##  Mean   :4.099e+07  
##  3rd Qu.:2.967e+07  
##  Max.   :1.444e+09  
## 
data <- data %>%
  mutate(date_aux = as.Date(date)) %>%
  filter(date_aux>"2020-01-01") %>%
  group_by(location, month(date_aux)) %>%
  filter(date_aux == max(date_aux))

data <- data %>%
    group_by(location) %>%
    fill(cases,
         deaths,
         vac1,
         vac2, .direction = c("down"))

data <- data %>%
        group_by(location) %>%
        mutate(cases = 100*replace_na(cases,0)/pop,
               deaths = 100*replace_na(deaths,0)/pop,
               vac1 = 100*replace_na(vac1,0)/pop,
               vac2 = 100*replace_na(vac2,0)/pop)

data<- data %>% mutate(date_num = as.numeric(date_aux)-18565 )


data <- data %>% select(-pop)
summary(data)
##          continent                  location        cases         
##               :  0   Afghanistan        :  12   Min.   : 0.00000  
##  Africa       :652   Albania            :  12   1st Qu.: 0.09272  
##  Asia         :573   Algeria            :  12   Median : 0.87755  
##  Europe       :595   Andorra            :  12   Mean   : 2.55694  
##  North America:359   Angola             :  12   3rd Qu.: 3.91616  
##  Oceania      :164   Antigua and Barbuda:  12   Max.   :21.30927  
##  South America:147   (Other)            :2418                     
##      deaths              vac1             vac2                 date     
##  Min.   :0.000000   Min.   :  0.00   Min.   :  0.000   2021-06-30: 219  
##  1st Qu.:0.001048   1st Qu.:  0.00   1st Qu.:  0.000   2021-07-31: 219  
##  Median :0.011024   Median :  0.00   Median :  0.000   2021-08-31: 218  
##  Mean   :0.045915   Mean   : 11.96   Mean   :  8.150   2021-05-31: 216  
##  3rd Qu.:0.068716   3rd Qu.: 13.22   3rd Qu.:  4.853   2021-04-30: 213  
##  Max.   :0.596713   Max.   :118.35   Max.   :117.132   2021-03-31: 210  
##                                                        (Other)   :1195  
##     date_aux          month(date_aux)     date_num    
##  Min.   :2020-10-31   Min.   : 1.000   Min.   :  1.0  
##  1st Qu.:2021-01-31   1st Qu.: 4.000   1st Qu.: 93.0  
##  Median :2021-04-30   Median : 6.000   Median :182.0  
##  Mean   :2021-04-20   Mean   : 6.425   Mean   :172.4  
##  3rd Qu.:2021-07-31   3rd Qu.: 9.000   3rd Qu.:274.0  
##  Max.   :2021-09-21   Max.   :12.000   Max.   :326.0  
## 
glimpse(data)
## Rows: 2,490
## Columns: 10
## Groups: location [223]
## $ continent         <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia~
## $ location          <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, ~
## $ cases             <dbl> 0.1037619, 0.1160148, 0.1313655, 0.1381258, 0.139860~
## $ deaths            <dbl> 0.003848333, 0.004425709, 0.005495109, 0.006024788, ~
## $ vac1              <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.02~
## $ vac2              <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.000000~
## $ date              <fct> 2020-10-31, 2020-11-30, 2020-12-31, 2021-01-31, 2021~
## $ date_aux          <date> 2020-10-31, 2020-11-30, 2020-12-31, 2021-01-31, 202~
## $ `month(date_aux)` <dbl> 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1~
## $ date_num          <dbl> 1, 31, 62, 93, 121, 152, 182, 213, 243, 274, 305, 32~
#data %>% filter_all(any_vars(is.na(.)))
names <- c('Brazil',
           'United States',
           'Canada',
           'Mexico',
           'Germany',
           'United Kingdom',
           'French',
           'Italy',
           'Spain',
           'Russia',
           'India',
           'South Korea',
           'China',
           'Japan',
           'Australia')


colors <- c('#F28B30', # Asia (laranja)
            '#BF0A3A', # Europa (vermelho)
            '#022873', # Am?rica do norte (azul)
            '#F23D6D', # Oceania (rosa)
            'gray',    # Outros (cinza)
            '#03A62C') # Am?rica do sul (verde)describe(data)
p <- data %>%
  ggplot(aes(x=cases,
             y=deaths,
             size=vac2)) +
  geom_point(aes(color=continent,
                 frame=date_num,
                 ids=location),alpha=0.6) +
  scale_size(range = c(.1, 24), name="fully vaccinated") +
  scale_colour_manual(values = colors) +
  xlim(-1, 20) +
  ylim(-0.07, .61) +
  theme_classic() +
  theme(legend.position = c(0.83, 0.86)) +
  guides(size = 'none') +
  labs(title="COVID-19 vaccinations of top 15 GPD countries")
## Warning: Ignoring unknown aesthetics: frame, ids

[ref animation plotly][https://plotly-r.com/animating-views.html]

ggplotly(p)
p
## Warning: Removed 3 rows containing missing values (geom_point).